Channel selection for heterogeneous nodes in cognitive networks

We propose algorithms to address the channel allocation and fairness issues of multi band multiuser cognitive ad-hoc networks. Nodes in the network have unequal channel access probability and have no prior information about the offered bandwidth or number of users in the multiple access system. In that nodes use reinforcement learning algorithm to predict future channel selection probability from the past experience and reach an equilibrium state. Proof of convergence of this multi party stochastic game is provided. Furthermore, analytical throughput for such system is determined. Finally, numerical results are presented for performance evaluation of the proposed channel allocation algorithms.